EasySpectra: An Integrated Open-Access Platform for Spectral Image Analysis

Spectral sensors have expanded the opportunities for the non-destructive monitoring of crops and weeds. However, the lack of standardized and accessible analytical pipelines remains a major limitation for data reproducibility and integration in this field. EasySpectra was developed to address these challenges by providing a unified environment that integrates data import, radiometric calibration, geometric alignment, spectral pre-processing, region-of-interest selection, feature extraction, vegetation index computation, and dataset construction. A graphical user interface guides users through the entire analytical workflow, reducing technical barriers for non-experts. EasySpectra supports heterogeneous data sources, including single-band images, spectral cubes and georeferenced orthomosaics. Across 100 sampled areas, the correction + normalization workflow in EasySpectra produced NDVI values very close to Pix4DFields (0.70 ± 0.052 vs. 0.69 ± 0.055), with a pixel-wise correlation of up to 0.98 and low bias (MBE = 0.05). In an independent UAV dataset, EasySpectra also showed close agreement with WebODM, with NDVI values ranging from 0.09 ± 0.10 to 0.42 ± 0.08 versus 0.08 ± 0.13 to 0.43 ± 0.10, across 13 sampled areas. In addition, hyperspectral species classification using EasySpectra-extracted profiles achieved a Macro F1-score of 0.880, with class-wise accuracies ranging from 0.83 for canola to 0.95 for redroot pigweed. Overall, EasySpectra enables reproducible, transparent, and standardized spectral analysis.